{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03ddf55d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "data_dir = 'DataRNN'\n",
    "fname = os.path.join(data_dir, 'MyDumpPT.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e7b2ed9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Date', ' Val']\n",
      "248\n"
     ]
    }
   ],
   "source": [
    "f = open(fname)\n",
    "data = f.read()\n",
    "f.close()\n",
    "\n",
    "lines = data.split('\\n')\n",
    "header = lines[0].split(',')\n",
    "lines = lines[1:]\n",
    "\n",
    "print(header)\n",
    "print(len(lines))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6cfd95e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "float_data = np.zeros((len(lines), len(header) - 1))\n",
    "for i, line in enumerate(lines):\n",
    "    values = [float(x) for x in line.split(',')[1:]]\n",
    "    float_data[i, :] = values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79824fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1090016a0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "temp = float_data[:, 0] # температура (в градусах Цельсия)\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.plot(range(len(temp)), temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d036dd50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a109099100>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "plt.plot(range(12), temp[:12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1bd76e38",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = float_data[:200].mean(axis=0)\n",
    "float_data -= mean\n",
    "std = float_data[:200].std(axis=0)\n",
    "float_data /= std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "98a7e1af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator(data, lookback, delay, min_index, max_index, \n",
    "              shuffle=False, batch_size=128, step=6):\n",
    "    if max_index is None:\n",
    "        max_index = len(data) - delay - 1\n",
    "    i = min_index + lookback\n",
    "    while 1:\n",
    "        if shuffle:\n",
    "            rows = np.random.randint(\n",
    "                min_index + lookback, max_index, size=batch_size)\n",
    "        else:\n",
    "            if i + batch_size >= max_index:\n",
    "                i = min_index + lookback\n",
    "            rows = np.arange(i, min(i + batch_size, max_index))\n",
    "            i += len(rows)\n",
    "        samples = np.zeros((len(rows),\n",
    "                            lookback // step,\n",
    "                            data.shape[-1]))\n",
    "        targets = np.zeros((len(rows),))\n",
    "        for j, row in enumerate(rows):\n",
    "            indices = range(rows[j] - lookback, rows[j], step)\n",
    "            samples[j] = data[indices]\n",
    "            targets[j] = data[rows[j] + delay][0]\n",
    "        yield samples, targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "10417581",
   "metadata": {},
   "outputs": [],
   "source": [
    "lookback = 24\n",
    "step = 1\n",
    "delay = 12\n",
    "batch_size = 3\n",
    "train_gen = generator(float_data,\n",
    "                      lookback=lookback,\n",
    "                      delay=delay,\n",
    "                      min_index=0,\n",
    "                      max_index=200,\n",
    "                      shuffle=True,\n",
    "                      step=step,\n",
    "                      batch_size=batch_size)\n",
    "val_gen = generator(float_data,\n",
    "                    lookback=lookback,\n",
    "                    delay=delay,\n",
    "                    min_index=201,\n",
    "                    max_index=236,\n",
    "                    step=step,\n",
    "                    batch_size=batch_size)\n",
    "test_gen = generator(float_data,\n",
    "                     lookback=lookback,\n",
    "                     delay=delay,\n",
    "                     min_index=237,\n",
    "                     max_index=None,\n",
    "                     step=step,\n",
    "                     batch_size=batch_size)\n",
    "\n",
    "val_steps = (236 - 201 - lookback) // batch_size\n",
    "test_steps = (len(float_data) - 237 - lookback) // batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "461f1a73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "500/500 [==============================] - 2s 2ms/step - loss: 0.2907 - val_loss: 0.2522\n",
      "Epoch 2/20\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2057 - val_loss: 0.2775\n",
      "Epoch 3/20\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1934 - val_loss: 0.2607\n",
      "Epoch 4/20\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1767 - val_loss: 0.2440\n",
      "Epoch 5/20\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1735 - val_loss: 0.4159\n",
      "Epoch 6/20\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1631 - val_loss: 0.4234\n",
      "Epoch 7/20\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1586 - val_loss: 0.3326\n",
      "Epoch 8/20\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1471 - val_loss: 0.3382\n",
      "Epoch 9/20\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1551 - val_loss: 0.6402\n",
      "Epoch 10/20\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1378 - val_loss: 0.3592\n",
      "Epoch 11/20\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1401 - val_loss: 0.4110\n",
      "Epoch 12/20\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1406 - val_loss: 0.3892\n",
      "Epoch 13/20\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1317 - val_loss: 0.3682\n",
      "Epoch 14/20\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1309 - val_loss: 0.5204\n",
      "Epoch 15/20\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1247 - val_loss: 0.4992\n",
      "Epoch 16/20\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1209 - val_loss: 0.3971\n",
      "Epoch 17/20\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1084 - val_loss: 0.4475\n",
      "Epoch 18/20\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1128 - val_loss: 0.5973\n",
      "Epoch 19/20\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1108 - val_loss: 0.4621\n",
      "Epoch 20/20\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1024 - val_loss: 0.5200\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras import layers\n",
    "from keras.optimizers import RMSprop\n",
    "\n",
    "model = Sequential()\n",
    "model.add(layers.Flatten(input_shape=(lookback // step, float_data.shape[-1])))\n",
    "model.add(layers.Dense(32, activation='relu'))\n",
    "model.add(layers.Dense(1))\n",
    "model.compile(optimizer=RMSprop(), loss='mae')\n",
    "\n",
    "history = model.fit(train_gen,\n",
    "                    steps_per_epoch=500,\n",
    "                    epochs=20,\n",
    "                    validation_data=val_gen,\n",
    "                    validation_steps=val_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "02fba468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "da7d2c05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "500/500 [==============================] - 8s 9ms/step - loss: 0.4203 - val_loss: 0.9254\n",
      "Epoch 2/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2829 - val_loss: 0.7067\n",
      "Epoch 3/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2637 - val_loss: 0.4807\n",
      "Epoch 4/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2684 - val_loss: 0.4987\n",
      "Epoch 5/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2518 - val_loss: 0.4776\n",
      "Epoch 6/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2478 - val_loss: 0.4720\n",
      "Epoch 7/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2375 - val_loss: 0.4604\n",
      "Epoch 8/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2255 - val_loss: 0.5615\n",
      "Epoch 9/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2272 - val_loss: 0.4772\n",
      "Epoch 10/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2263 - val_loss: 0.4066\n",
      "Epoch 11/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2196 - val_loss: 0.6464\n",
      "Epoch 12/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2145 - val_loss: 0.6755\n",
      "Epoch 13/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2145 - val_loss: 0.5065\n",
      "Epoch 14/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2071 - val_loss: 0.6015\n",
      "Epoch 15/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2115 - val_loss: 0.5675\n",
      "Epoch 16/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.1937 - val_loss: 0.5512\n",
      "Epoch 17/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.1946 - val_loss: 0.6071\n",
      "Epoch 18/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.1910 - val_loss: 0.5830\n",
      "Epoch 19/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.1871 - val_loss: 0.6005\n",
      "Epoch 20/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.1888 - val_loss: 0.7056\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(layers.GRU(32, input_shape=(None, float_data.shape[-1])))\n",
    "model.add(layers.Dense(1))\n",
    "\n",
    "model.compile(optimizer=RMSprop(), loss='mae')\n",
    "history = model.fit(train_gen,\n",
    "                    steps_per_epoch=500,\n",
    "                    epochs=20,\n",
    "                    validation_data=val_gen,\n",
    "                    validation_steps=val_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "aad0acca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b0f91205",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "500/500 [==============================] - 9s 10ms/step - loss: 0.4244 - val_loss: 0.8662\n",
      "Epoch 2/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.3266 - val_loss: 0.6280\n",
      "Epoch 3/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.3195 - val_loss: 0.5364\n",
      "Epoch 4/40\n",
      "500/500 [==============================] - 5s 10ms/step - loss: 0.3032 - val_loss: 0.5991\n",
      "Epoch 5/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.3066 - val_loss: 0.6129\n",
      "Epoch 6/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2941 - val_loss: 0.5678\n",
      "Epoch 7/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2830 - val_loss: 0.4342\n",
      "Epoch 8/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2963 - val_loss: 0.6942\n",
      "Epoch 9/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2851 - val_loss: 0.8008\n",
      "Epoch 10/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2726 - val_loss: 0.5929\n",
      "Epoch 11/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2838 - val_loss: 0.5290\n",
      "Epoch 12/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2782 - val_loss: 0.5869\n",
      "Epoch 13/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2737 - val_loss: 0.5746\n",
      "Epoch 14/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2658 - val_loss: 0.8026\n",
      "Epoch 15/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2690 - val_loss: 0.6437\n",
      "Epoch 16/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2683 - val_loss: 0.5628\n",
      "Epoch 17/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2621 - val_loss: 0.5240\n",
      "Epoch 18/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2586 - val_loss: 0.5057\n",
      "Epoch 19/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2542 - val_loss: 0.5189\n",
      "Epoch 20/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2606 - val_loss: 0.5806\n",
      "Epoch 21/40\n",
      "500/500 [==============================] - 5s 10ms/step - loss: 0.2596 - val_loss: 0.5921\n",
      "Epoch 22/40\n",
      "500/500 [==============================] - 5s 10ms/step - loss: 0.2587 - val_loss: 0.5323\n",
      "Epoch 23/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2557 - val_loss: 0.5104\n",
      "Epoch 24/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2596 - val_loss: 0.5670\n",
      "Epoch 25/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2577 - val_loss: 0.6848\n",
      "Epoch 26/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2511 - val_loss: 0.6007\n",
      "Epoch 27/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2486 - val_loss: 0.5134\n",
      "Epoch 28/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2441 - val_loss: 0.4471\n",
      "Epoch 29/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2458 - val_loss: 0.6202\n",
      "Epoch 30/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2371 - val_loss: 0.5400\n",
      "Epoch 31/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2407 - val_loss: 0.5049\n",
      "Epoch 32/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2421 - val_loss: 0.5271\n",
      "Epoch 33/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2371 - val_loss: 0.4793\n",
      "Epoch 34/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2373 - val_loss: 0.5895\n",
      "Epoch 35/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2378 - val_loss: 0.5269\n",
      "Epoch 36/40\n",
      "500/500 [==============================] - 5s 10ms/step - loss: 0.2358 - val_loss: 0.5270\n",
      "Epoch 37/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2287 - val_loss: 0.5086\n",
      "Epoch 38/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2385 - val_loss: 0.5461\n",
      "Epoch 39/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2253 - val_loss: 0.4985\n",
      "Epoch 40/40\n",
      "500/500 [==============================] - 5s 9ms/step - loss: 0.2298 - val_loss: 0.5698\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(layers.GRU(32,\n",
    "                     dropout=0.2,\n",
    "                     recurrent_dropout=0.2,\n",
    "                     input_shape=(None, float_data.shape[-1])))\n",
    "model.add(layers.Dense(1))\n",
    "model.compile(optimizer=RMSprop(), loss='mae')\n",
    "\n",
    "history = model.fit(train_gen,\n",
    "                    steps_per_epoch=500,\n",
    "                    epochs=40,\n",
    "                    validation_data=val_gen,\n",
    "                    validation_steps=val_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8199f55b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c724bdf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "500/500 [==============================] - 18s 21ms/step - loss: 0.4485 - val_loss: 0.8114\n",
      "Epoch 2/40\n",
      "500/500 [==============================] - 10s 19ms/step - loss: 0.3496 - val_loss: 0.7477\n",
      "Epoch 3/40\n",
      "500/500 [==============================] - 10s 19ms/step - loss: 0.3278 - val_loss: 0.5987\n",
      "Epoch 4/40\n",
      "500/500 [==============================] - 10s 19ms/step - loss: 0.3073 - val_loss: 0.4627\n",
      "Epoch 5/40\n",
      "500/500 [==============================] - 10s 19ms/step - loss: 0.2982 - val_loss: 0.4147\n",
      "Epoch 6/40\n",
      "500/500 [==============================] - 10s 19ms/step - loss: 0.2983 - val_loss: 0.5262\n",
      "Epoch 7/40\n",
      "500/500 [==============================] - 10s 19ms/step - loss: 0.2833 - val_loss: 0.5229\n",
      "Epoch 8/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2800 - val_loss: 0.4948\n",
      "Epoch 9/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2693 - val_loss: 0.3171\n",
      "Epoch 10/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2733 - val_loss: 0.4084\n",
      "Epoch 11/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2592 - val_loss: 0.5377\n",
      "Epoch 12/40\n",
      "500/500 [==============================] - 10s 19ms/step - loss: 0.2612 - val_loss: 0.5715\n",
      "Epoch 13/40\n",
      "500/500 [==============================] - 10s 19ms/step - loss: 0.2580 - val_loss: 0.4482\n",
      "Epoch 14/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2593 - val_loss: 0.5822\n",
      "Epoch 15/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2521 - val_loss: 0.5786\n",
      "Epoch 16/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2447 - val_loss: 0.5607\n",
      "Epoch 17/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2511 - val_loss: 0.6147\n",
      "Epoch 18/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2350 - val_loss: 0.7501\n",
      "Epoch 19/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2490 - val_loss: 0.6315\n",
      "Epoch 20/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2399 - val_loss: 0.5819\n",
      "Epoch 21/40\n",
      "500/500 [==============================] - 10s 21ms/step - loss: 0.2493 - val_loss: 0.5990\n",
      "Epoch 22/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2374 - val_loss: 0.5403\n",
      "Epoch 23/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2373 - val_loss: 0.6065\n",
      "Epoch 24/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2266 - val_loss: 0.6612\n",
      "Epoch 25/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2359 - val_loss: 0.5517\n",
      "Epoch 26/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2264 - val_loss: 0.7191\n",
      "Epoch 27/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2341 - val_loss: 0.7242\n",
      "Epoch 28/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2370 - val_loss: 0.6814\n",
      "Epoch 29/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2274 - val_loss: 0.6726\n",
      "Epoch 30/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2233 - val_loss: 0.5906\n",
      "Epoch 31/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2291 - val_loss: 0.7225\n",
      "Epoch 32/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2232 - val_loss: 0.7638\n",
      "Epoch 33/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2247 - val_loss: 0.5966\n",
      "Epoch 34/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2189 - val_loss: 0.6807\n",
      "Epoch 35/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2172 - val_loss: 0.7678\n",
      "Epoch 36/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2045 - val_loss: 0.7770\n",
      "Epoch 37/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2118 - val_loss: 0.6478\n",
      "Epoch 38/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2101 - val_loss: 0.6180\n",
      "Epoch 39/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2099 - val_loss: 0.5531\n",
      "Epoch 40/40\n",
      "500/500 [==============================] - 10s 20ms/step - loss: 0.2112 - val_loss: 0.6573\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(layers.GRU(32,\n",
    "                     dropout=0.1,\n",
    "                     recurrent_dropout=0.5,\n",
    "                     return_sequences=True,\n",
    "                     input_shape=(None, float_data.shape[-1])))\n",
    "model.add(layers.GRU(64, activation='relu',\n",
    "                     dropout=0.1,\n",
    "                     recurrent_dropout=0.5))\n",
    "model.add(layers.Dense(1))\n",
    "\n",
    "model.compile(optimizer=RMSprop(), loss='mae')\n",
    "history = model.fit(train_gen,\n",
    "                    steps_per_epoch=500,\n",
    "                    epochs=40,\n",
    "                    validation_data=val_gen,\n",
    "                    validation_steps=val_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "99d4ad88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0NERERERE7tChQwdMnz79jG2jRo3CHXfckeNz/Et0devWDQcOHDjrMcOHD8fIkSNzPPakSZPw66+//nv/6aefxowZM/IQfdbmzJmD7t27F3g/VghowWtjzFRjTANjzHnGmBG+bW8bY972ff6nMeZKY0wTY8zFxpjxdgYdbNdeC8TGAo89BvyZueiTiIiIiCgMDRgwABMmTDhj24QJEzBgwICAnj916lSUL18+X8fOnKj95z//QadOnfK1L7cKKFELd/611U6dAu65x+loiIiIiIic17dvXyQnJ+PEiRMAgM2bN+PPP//EpZdeittvvx1RUVG46KKL8Mwzz2T5/Lp162LvXi3CGzFiBC688EJ06tQJ69ev//cx7777Llq1aoVmzZqhT58+OHr0KBYsWIDJkyfjoYceQmRkJH7//XcMGTIEX331FQBg5syZaN68OZo0aYIbb7zx3/jq1q2LZ555Bi1atECTJk2wbt26HL+//fv3o2fPnmjatCnatGmDVatWAQB+/PFHREZGIjIyEs2bN8fhw4exc+dOtG/fHpGRkbj44ouRkpJSsB8ugMIF3kOYOO884OmngccfB5KSgKuucjoiIiIiIiJ1333AypXW7jMyEhg1KvuvV6pUCa1bt8Z3332H+Ph4TJgwAddccw1EBCNGjEDFihWRlpaGjh07YtWqVWjatGmW+1m2bBkmTJiAFStWIDU1FS1atEDLli0BAL1798Ytt9wCAHjyySfx/vvv4+6770aPHj3QvXt39O3b94x9HT9+HEOGDMHMmTPRoEEDDB48GG+99Rbuu+8+AEDlypWxfPlyjB07FiNHjsR7772X7ff3zDPPoHnz5pg0aRJmzZqFwYMHY+XKlRg5ciTGjBmDdu3a4ciRIyhevDjGjRuHzp0744knnkBaWhqOHj0a8M85OxxRy4MHHgAuugi46y7gyBGnoyEiIiIiclbG8seMZY9ffPEFWrRogebNm2PNmjVnlClmlpKSgl69eqFkyZIoW7YsevTo8e/XVq9ejZiYGDRp0gQJCQlYs2ZNjvGsX78e9erVQ4MGDQAA119/PebOnfvv13v37g0AaNmyJTZv3pzjvubNm4dBgwYBAK644grs27cPBw8eRLt27TBs2DCMHj0aBw4cQOHChdGqVSt8+OGHGD58OH755ReUKVMmx30HgiNqeVC0qK6tdumlwDPPAK++6nREREREREQ5j3zZqWfPnhg2bBiWL1+OY8eOoUWLFvjjjz8wcuRILFmyBBUqVMCQIUNw/PjxHPcjktXSzcCQIUMwadIkNGvWDB999BHmzJmT436Mybzc85mKFSsGAIiIiEBqamqe9yUiePTRRxEXF4epU6eiTZs2mDFjBtq3b4+5c+diypQpGDRoEB566CEMHjw4x/3nhiNqedSunbbqHzUKWLHC6WiIiIiIKNQZA2zcCHzyCfDDD05Hc6bSpUujQ4cOuPHGG/8dTTt06BBKlSqFcuXKYdeuXZg2bVqO+2jfvj2+/fZbHDt2DIcPH0ZSUtK/Xzt8+DCqV6+OU6dOISEh4d/tZcqUweHDh8/aV8OGDbF582Zs3LgRAPDpp5/isssuy9f31r59+3+POWfOHFSuXBlly5bF77//jiZNmuCRRx5BVFQU1q1bhy1btqBq1aq45ZZbcNNNN2H58uX5OmZGHFHLhxdeACZNAoYOBRYuBCIinI6IiIiIiELFyZPA8uXA/Pn6sWABsGuXfq1MGWDPHsA3MOQKAwYMQO/evf8tgWzWrBmaN2+Oiy66CPXr10e7du1yfH6LFi1wzTXXIDIyEnXq1EFMTMy/X3vuuedwySWXoE6dOmjSpMm/yVn//v1xyy23YPTo0f82EQGA4sWL48MPP8TVV1+N1NRUtGrVCrfddttZxwzE8OHDccMNN6Bp06YoWbIkPv74YwC6BMHs2bMRERGBxo0bo2vXrpgwYQJeeeUVFClSBKVLl8Ynn3ySr2NmJLkND9olKirK+NdQ8KKJE4H+/YE33mAnSCIiIiLKv/37NRnzJ2ZLlgD+SsH69bWiq107IDVVeyVMmwZ06QKsXbsWjRo1cjZ4ClhWr5eILDPGRGX1eI6o5VO/fsBHHwFPPAH07g3UrOl0RERERETkdv4yRn9SNn8+sHatfq1wYaBFC+D22zUxi44Gqlc//dzjx4FHHgESEzVRo9DGRC2fRICxY7UL5N13A99+63REREREROQ2J06cXca4e7d+rXx5Tcauu04Ts1atgJIls99X8eJA587A5MnAmDFBCZ8cxEStAOrVA4YP1ysbkyYBPXs6HBARERERucaWLZp87dmj9887T0fC/KWMjRoBhfLY2i8+HvjmG2DZMqB0aetjJvdgolZA998PJCToqFrHjjrBk4iIiIhyZoxWKIWy114D/v5bexu0bw9Uq1bwfcbFaSO7xERg4EBtIZ9da3tyj/z0BWF7/gIqUkTXVtuxA3jqKaejISIiInK3o0eB7t2BXr2cjsRe+/YB772nyVS/ftYkaQBQqRIQE6OJWvHixbFv3758JQEUPMYY7Nu3D8WLF8/T8ziiZoE2bXTS5//+pzXGUVn2bSEiIiIKbydOaIL2/fc6KnTkSOiW7731lialDz5o/b7j47WqKzW1Jg4f3o49/tpKcq3ixYujZh67D7I9v0UOHtQ642rVgMWLtWsPEREREamTJ4G+fYGkJGDQIODTTzVhi411OjLrHT8O1KkDtGwJTJ1q/f7/+EPb9r/6KjBsmPX7p+DJqT0/Sx8tUq4cMHo0sGKFjqwRERERkUpN1RLApCTtVjhmjDbRSElxOjJ7fPKJdnZ8+GF79l+vHtCkiZY/UuhiomahPn10gudTTwFbtzodDREREZHz0tKA668HvvpKm2vccYc2X2veHJg71+norJeWBowcqVNhLrvMvuPExwPz5gF799p3DHIWEzULiegVImOAO+/UWyIiIqJwlZ4ODB0KfPYZMGKEzqvyi4kBFi3SeWuhZPJkYMMGHU2zsxljfLz+fKdMse8Y5CwmaharUwf4z3+A5GRd44KIKBRt2gT89ZfTURCRmxmjyxd98IFWGz3++Jlfj4nRuVzLljkTnx2MAV5+WeeP9e5t77FatgRq1GD5YyhjomaDe+8FIiP1j9PBg05HQ0RkLWN03chBg5yOhIjcyhjggQeAsWOBhx4Cnn327MdceqnehtI8tfnzgYULtcFHRIS9xxLRUbXp04Fjx+w9FjmDiZoNChcGxo3Tq82jRzsdDRGRtX75Bdi8GZg1C9i1y+loiMiNnnwSeP11vWj90ktZlwBWrQpceGFoJWovv6zrnN1wQ3COFx+vSwDMnBmc41FwMVGzSatWQOvWwLRpTkdCRGSt5GS9TU8Hvv7a2ViIyH3++1/g+eeBW24B3ngj53la7dvrKFR6evDis8vatdrV8q67gJIlg3PMDh2AsmWBSZOCczwKLiZqNoqN1TXVWP5IRKFkyhSdG9G4MfDFF05HQ0Ru8sorOh9t0CDg7bdzb6YREwMcOACsXh2U8Gz16qtAiRLaUC5YihYFunbVBDEtLXjHpeBgomajTp30TTNnjtOREBFZY+9e4KefdCmSfv20tfbOnU5HRURu8L//aafDa67RBiKFAjjLjInRW6+XP+7cqQt433ADUKVKcI8dH69rti1aFNzjkv2YqNmobVsd+v7hB6cjISKyxrRp2iSge3dN1Ixh+SMR6dz8e+4BevbUhKVw4cCeV6cOULOm9xO10aN1Ue9hw4J/7K5d9efN7o+hh4majYoW1YUOZ8xwOhIiImtMmQKcc46WPjZqBDRpwvJHonD3ySfAbbdpwjBhAlCkSODPFdFRtblzvbv+7OHDwFtvAX36AOedF/zjly8PXH45E7VQxETNZrGxwPr1wLZtTkdCRFQwp04B330HdOt2uqSpXz9g3jxgxw5nYyMiZ0ycqOV+V1yho+vFiuV9HzExWjq4aZP18QXDu+9qP4KHHnIuhvh4Pd9cv965GMh6TNRsFhurtyx/JCKvW7BAT0a6dz+97eqr9Sr4V185FxcROePbb4GBA4F27XQ0p0SJ/O3Hy/PUTp3SZQg6dNCO307p0UNvOaoWWpio2eyii4Bq1Vj+SETel5ysJU3+C1CAroHUrBnLH4nCzdSp2jSkVSstiS5VKv/7atwYqFDBm4nahAnA9u3OjqYBQK1aQIsWbNMfapio2UxEuz/OmBEaa4QQUfiaMkXn3ZYpc+b2a67R0TaWeBOFh5kzgd69dY7qtGln/03Iq0KFdFTNa4maMbocwcUX6/w8p8XHAwsXArt2OR0JWYWJWhDExgJ79gCrVjkdCRFR/mzapIu5xsWd/bWrr9Zblj8Shb65c4GrrgIaNAC+/14bWVghJgbYsAH46y9r9hcM06cDv/wCPPhg7uvFBUN8vCaPSUlOR0JWYaIWBB076i3LH4nIq6ZM0duM89P8zj9fS24mTgxuTES5MQbYtw9YulQvJLzyCnDHHcD113u3cYWTFi7UizV16ujc+0qVrNu3f57avHnW7dNur7wC1KgBDBjgdCSqaVN9bThPLXQEuMoFFUSNGlp//cMPetWFiMhrkpP1Cvr552f99X79gEcfBTZvBurWDWZkFO4OHQL++EN/9/7448zPN2/W1ukZVagAnDypZXbz5gHnnutA0B60cyfQpYsuzzFzpt5aqUULXXs2JQXo29fafdth2TJg1ixN1ooWdToaJaLr2L3zDvDPPwWbN0juwEQtSDp10sUgjx8Hihd3OhoiosAdOQLMmQPcdVf2j7n6ak3UvvqKF6TIevv362hOVgnZ/v1nPrZ0aaBePb1gcPnlpz/335YrByxZou3kr7wS+PFHa0eGQtW332rX15QUe5LbIkWANm20tNILXnkFKFsWGDrU6UjOFB8PvPGGlqX26uV0NFRQTNSCJDZWV61fsED/ORARecWMGToCkdX8NL/69YGoKO3+yESNrNavn47iALpOlz/xuuSS05/7E7FKlXKfL9SqFTB5sjaA6NZNf8cL2hAj1E2erKPqTZrYd4yYGOA//9GEsFw5+45TUJs2AV9+qX/rypZ1OpozxcToqHFiIhO1UMBELUguuwwoXFjLH5moEZGXJCfrycill+b8uGuu0RbVf/yhJ81EVjh+XEdxbrwR+O9/teSukAUz7C+/XC8s9O6t5WJTprDiJTuHDwOzZwN3323vcWJidF7hggXu6KKYnddfByIigHvvdTqSsxUurBfVkpKA1FS9T97FZiJBUqaMDumzoQgReUl6uq6X1Llz7vMw/N0fuaYaWWnJEh3R7dEDqF7dmiTNr0cP4MMPda7RgAF6Yktn+/7706+Bndq00cTCzW369+4F3n9fF/p26/zG+HgtCZ4/3+lIqKCYqAVRbKxOPt23z+lIiIgCs2KFNhHIqezRr04dLUVjokZW8p+05zaim1+DBunUhEmTgJtv5pqnWUlK0nK66Gh7j1OqlDYVcXOiNnYscOyYu0u8/RfW2P3R+5ioBVFsrA7pz5rldCRERIGZMkXn+wRahtSvH7B8ObBxo71xUfhISdHOyXY2/Lj7buDZZ4GPPwaGDdP/1aTS0rT8OS4uOGV07dsDixdryavbHDsG/O9/+rO46CKno8lemTK6NFRiIn+XvY6JWhC1aqXzPFj+SERekZwMtG4NVK0a2OP95Y9ffmlfTBQ+0tJ0vpJ/jS07PfUUcN992jHvuefsP55X/PSTVgJddVVwjhcTo2WWS5YE53h58dFHWvr48MNOR5K7nj216cmaNU5HQgURUKImIl1EZL2IbBSRR7P4+kMistL3sVpE0kSkovXhelvhwjp5+YcfnI6EiCh3u3bpyVJWi1xnp1YtLY9i+SNZYdUqXSctGImaCPDqq8CQIcAzz2g5JGnZY5EiWk4XDO3a6a3byh/T0vT3o3Xr4Pw+FpQ/sWb5o7flmqiJSASAMQC6AmgMYICINM74GGPMK8aYSGNMJIDHAPxojNl/1s4InTppR7Tff3c6EiKinE2dqreBzE/LqF8/YOVK4LffLA+Jwoz/ZD1YJ8aFCgHvvqttze+9F/jkk+Ac180mT9bO1cFql1+pkpYVum09tW+/1XO3hx/OffkHN6heXecMM1HztkBG1FoD2GiM2WSMOQlgAoD4HB4/AMDnVgQXimJj9Zblj0TkdlOmaFezyMi8Pa9vX73lqBoVVEoKULu2fgRL4cLAZ5/pHJ8bbwzvE90NG4B16+zv9phZTIyWvKalBfe42TFGF7g+/3wtKfSK+Hititixw+lIKL8CSdRqANiW4f5237aziEhJAF0AfF3w0EJTgwZaGsTyRyJys5MntSV3XFzerx7XqKEd+pioUUEYo4maE2VmxYtrF8ioKB0hDtcmYElJehus+Wl+MTG6dtvPPwf3uNlJSdEGJ8OG6fppXhHvG1aZPNnZOCj/AknUsvoXnV0PmasAzM+u7FFEhorIUhFZumfPnkBjDCkiWv44a5Z7rhQREWWWkqInSnmZn5ZRv37AL78Aa9daGxeFj40bdZ6kU/OBSpfW8t8LLtAT3sWLnYnDSUlJQJMmQN26wT2u/zV3yzy1l18GqlTR+Yte0qiRjgKG86iw1wWSqG0HUCvD/ZoA/szmsf2RQ9mjMWacMSbKGBNVpUqVwKMMMbGxwN9/awtrIiI3Sk4GihXT8q/86NNHL0yx+yPlV7Dnp2WlYkUdWa5aVZeoCKcOen//ra9BsEfTAK08qlPHHYnamjVaBn7XXUCJEk5HkzciepFh1ixtykPeE0iitgTABSJST0SKQpOxswZRRaQcgMsAMG/Phf/Eh+WPRORWycnapbZUqfw9/9xzdT2kiROtjYvCR0qKNpZo1MjZOM49V/9fFysGXHmlNgQLB9OmaeVPsOen+bVvr78DTq8DNnIkULIkcOedzsaRXz17AqdOAd9953QklB+5JmrGmFQAdwGYDmAtgC+MMWtE5DYRuS3DQ3sB+N4Y8489oYaOqlWBZs3YUISI3Om337TsLL9lj379+gG//hpeoxBknZQUnevohg579evryNqxYzp9YedOpyOy3+TJwDnn6BqwToiJAXbv1oYmTtmxA0hI0KYydi64bqe2bbVsk+WP3hTQOmrGmKnGmAbGmPOMMSN82942xryd4TEfGWP62xVoqImNBebPB44edToSIqIzJSfrbV7b8mfWu7e2O2dTEcqrnTu1FXr79k5HctrFF+so065duqbY/hBehOjkSR2B6d5d38NOcMM8tTfe0FHFYcOci6GgIiL0dZw6VUfWyFscevtRp076h9Bt64QQEU2ZousYFbSBQLVquv7SF184X75E3uKG+WlZ8a9LtX69Xsg4csTpiOyRkgIcPOjM/DS/Cy/UkSCnzpMOHQLeeQe4+mqgXj1nYrBKfDxw4ADPOb2IiZpDYmKAokVZ/khE7nLwoP4zL+homl+/froO0+rV1uyPwkNKis6PbN7c6UjO1rEjMGGCdoHs3Rs4ccLpiKyXlKRLFHTq5FwMIlr66tSI2rhxmqw99JAzx7dSbKw2Qpk0yelIKK+YqDmkZEn9A8SGIkTkJj/8AKSmFnx+ml+fPlo6xaYilBdz5+rcmsKFnY4ka716Ae+/r++Xa6/V90yoMEbnp3XsmP9mQlaJidHmLcFesPnkSWDUKOCKK4CWLYN7bDuULKnJWmIiqxu8homagzp1Alat0np3IiI3SE4GKlTQk2QrVKmiJzssf6RAHTiga/C5rewxsyFDgNdfB775RjukjhihbdC9Xg7566+aHDnV7TEjp+apff65JoehMJrmFx8PbNsGrFzpdCSUF0zUHBQbq7czZzobBxERAKSn64TzLl2sHcno1087t/38s3X7pNA1f74m9W5P1ADgvvt05GXvXuDJJ3UUqlw57ex8223Axx/rfDYvXaSY7FuAyapR9YKIjNSFx4OZqKWnAy+9pAt9d+4cvOPa7aqrtLqB3R+9hYmag5o318U0Wf5IRG6wZAmwZ49189P8evXSzmPs/kiBSEkBihTRxh1ecO+9wNq12gVy2jRN2KpV03lsQ4YADRsClSvr++q553RuupsXH05KAqKidP04pxUuDERHBzdRmzxZX89HH3XH0hBWqVJFf5ZM1LyFiZqDIiK0JGjGDG9dbSOi0DRlil5x7dLF2v1WrqwjDSx/pECkpOi8oJIlnY4kbypU0PfOs88C06dr4rZ6NfDee3qxYssW4JlntJqmfHkdsRk6FPjgA00M0tOd/g503bKFC53t9phZTIz+HP/+2/5jGQM8/7yum9evn/3HC7b4eC193LLF6UgoUEzUHBYbC2zfrqURREROSk7WuWl2LOzar5+ui7V8ufX7ptBx7JiO7Hqh7DE3hQrpMhc33aTJ2urVmrxNnw4MHw7UrAl8+aV+vXFjfd916QK8+CJw/LgzMU+ZosmKG+an+cXEaEzz59t/rFmz9Pfv4Yfd28imIOLj9dZf3krux0TNYf55aix/JCIn7dgBrFhh37yUXr30xIflj5STxYt1Ud5QSNSyUr48cOWVwNNPa5nkvn06mvbBB7pe159/Ao89Brz6qjPxTZ4M1Kqlc+zconVrLYUNxhpgzz8PVK8OXH+9/cdywgUXAI0asU2/lzBRc1i9ejrEzvXUiMhJU6fqrV2JWsWKemGK5Y+UE/9cpHbtnI0jWAoV0jlsN9yg63atWgV066bdJIPdPfL4ceD777Xs0U1zs0qUAFq1sn+e2qJFOqI2bJiuIReq4uOBH38MTikpFRwTNReIjQVmz9ariERETkhOBmrX1lItu/TrB2zeDCxdat8xyNtSUoCLL9bEPlw99ZSOtL31VnCPO2sWcPSou+an+cXE6N+No0ftO8YLL+g8w1tvte8YbtCzJ5CWdvriHLkbEzUX6NQJOHxYSz6IiILt+HEd1e/e3d4r6fHxWsLE8kfKSmoqsGBB6JY9BqpNGz0vGDlS5+wFS1KStsK//PLgHTNQMTH6+7FokT37X7NGuyHefTdQpow9x3CLVq20vJPdH72BiZoLXHGFnhyx/JGInDBnjl6ptrotf2YVKuj8HJY/UlZ+/lnL/cI9UQN0VG33buDdd4NzPGM0UevcGShWLDjHzIt27fQ8ya7yx5deAkqVAu65x579u0mhQjpqOm0acOKE09FQbpiouUDFirpmCRuKEJETpkzReSDBuJJ+zTXA1q32XRmn/DEGuPlmXTvKKf6TcCZqQPv2+nN4+eXgnEyvWKENhdxY9gicXs7AjkTtjz+Azz7TpRLs6HjrRvHxelFk9mynI6HcMFFziU6ddO0SNy+CSUShxxidn9axoyZrduvRAyhalOWPbjNqFPD++9rEwqkmAykp2mCrZk1nju82Tz2lydOHH9p/rMmTdaSlWzf7j5Vf7dsDP/2kJZBWGjlSv/cHHrB2v252xRU6ghhO5Y8HDgAnTzodRd4xUXOJ2Fid3Pnjj05HQkThZO1abfBhV7fHzMqV07WivvzSHQv8ErBsGfDII0CLFnoi8+WXwY/BGE3UOJp2WqdOwCWX6LpqdjcbS0rSNRSrVLH3OAUREwP884+O/lnlr7/0AsX11wM1ali3X7crXlz/Dicmhs/f4Wef1S7rXkvWmKi5RHS0Xs1m+SMRBVNyst4G80p6v37A9u1aRUDOOnwY6N8fOOccbc3eqBHw6afBj+O334A9e5ioZSSio2pbtgDjx9t3nO3bdSF6Ny1ynRX/74aV5Y+jRmkS/PDD1u3TK+LjgZ07w6ML78mT+nctOlorOryEiZpLFCumw/psKEJEwTRlii5uW6tW8I551VX6N2/ixOAdk7J2553Apk1AQoLOzxk8GJg3T7cFE+enZa1bN6B5c12I2eqSPz//xRq3zk/zq14dOO886xa+PnAAGDtWFxq/4AJr9uklcXFARER4lD8mJemSFzfe6HQkecdEzUViY7UMaft2pyMhonCwfz8wf37wyh79ypYFunZl+aPTPv1UP556Si8UAsDAgTqSY+cITlZSUoCqVYEGDYJ7XLcTAZ58Eti40b4LG5MnA+efrwtvu11MjF5IsOLvxpgxOqLsZAMdJ1WsqO/7cEjUPvhAS1tjY52OJO+YqLlIp056y1E1IgqG6dN1bqzdbfmzcs01WnYzf37wj03Ahg3AHXfoie+TT57eXqsW0KGDJnDBXEJh7lzg0kvtXcfPq3r21EXAR4yw/sLGkSO60PVVV3njZx8ToyMj69YVbD9Hj2rZY7duQGSkFZF5U3y8riG3YYPTkdhnxw7gu++AIUN0BNFrmKi5SJMmekWRiRoRBcOUKUDlykDr1sE/dvfuOqGd3R+D7+RJYMAAXXw8IQEoXPjMrw8erCM4wZpDuH27NrRh2WPWChUCnnhCK26++cbaff/wg7b/d/v8ND+r5qm99x6wdy/w2GMFj8nLevXSOVtdu+o8xVD08cd6gWPIEKcjyR8mai5SqJCOqs2YwcVgicheaWm64GnXrs5cZSxdWkfyvvpKY6Hgeewx7fT44YdZz03s00ebWwWrqQjnp+Xu6quBCy8E/vtfa88PkpJ0jbJ27azbp53OP18b3xQkUTt5Ulvyx8ToKG44q11bR1RPnNCun2++GVrnn8Zo2eNll+nvjhcxUXOZTp2AXbuAX35xOhIiCmULF+octWDPT8uoXz9tj23HIraUtWnTgNde0yYi8fFZP6ZMGS23mzgxOIstp6Ro4t6smf3H8qqICODxx4Gff9bkygppadpIpFs3HV31AhGdV1WQvxkJCcC2bRxN82vXDli5ErjySuDuu4G+fbXRSihISQF+/92bTUT8mKi5jH+iI8sfichOycl68nfllc7FEBcHlCzJ8sdg2blT14tq0gR45ZWcHzt4sCbyU6faH1dKirbNzlyCSWcaMEAXBH/uOWtGPRYv1iUR3N7tMbOYGGDrVv3Iq7Q04KWXdF5aly6Wh+ZZlSppU5mRI/W2RQtgyRKnoyq4Dz7QC099+jgdSf4xUXOZmjW18xLXUyMiO02Zoic85cs7F0OpUjqi9/XX9rUeJ5WeDgwapM0jJk7U0sacdOqkJWZ2lz/u3w+sXs2yx0AUKaKjQEuXaiOggpo8WZNjryUsBZmn9u23wPr1+nP0QvOUYBIBHnhAf67p6TrSNmqUd0shDx3SzsL9++v/Gq9iouZCnToBP/4YnJITIgo/W7dqebUT3R4z69cP2L3burWRKGsvvwzMnAmMHq2LWuemcGHg2mt15HX/fvvi8nf9ZKIWmOuv13mFVoyqJSVpGaGTF2vyo0kTXeIjr38zjAFeeEHXTPPyCIvd2rQBVqzQktj779cyaDv/Btjliy+0u6eXyx4BJmquFBsLHDsG/PST05EQUSiaMkVvnZyf5te1q17tZPmjfRYu1Bb8/foBN90U+PMGDwZOnbJ3YfKUFB0pcqLzqBcVLQo88giwYAEwZ07+97Npk7Zl90q3x4wiInS0J68jat9/r50NH3nEm23ag6lCBR19HDVK57U2b+69c9IPPgAaNwYuucTpSAqGiZoLXXaZ/hFh+SMR2SE5GahfX7vIOa1kSZ0jw/JHexw4oHObatUCxo3LW7lXs2a6fped5Y8pKUCrVrmXYtJpN94IVKumo2r55W9I4rX5aX4xMbpcwd69gT/nhRd0esmgQfbFFUpEgHvv1VHviAgdfX3lFevX8rPD2rWaWN54o/dLXJmouVC5cnoFgA1FiMhqR49qO+bu3d3zD2zgQD3hKsiJJ53NGODWW7XD3eef6/+WvBDRk9qfftJ11ax29KjOt2LZY96UKAE89BAwe3b+F4yfPBm46CK9YONF/t+ZefMCe/yCBTql5IEHdFSSAteqlY5ExscDDz+so7B5SZCd8OGHWr593XVOR1JwTNRcKjZW/4H9/bfTkRBRKJk1Czh+3B1lj35xccANNwD/+Y+W25A13n9fS0qfe07nneTHwIGasNkxqrZokY6itm9v/b5D3a236mL1//1v3p974IDO7/LqaBqgyUOxYoGXP77wgnY2vOUWe+MKVeXLa2OON9/Uaq/mzQNPkoPt1Cngk0/0f9w55zgdTcExUXOpTp10eHnWLKcjIaJQMmWKzglz08mxCDB2rM5TGjxY585QwaxdC9xzD9Cxo87Jya8aNXQf48db3/0tJUVf++hoa/cbDkqV0tGh777Lexv1777TBNmL89P8ihXTyqNAErVVq7Tc+957vd39z2kiuv7iTz/pz79DB02A3VYKOW2arkfs9SYifkzUXOqSS3TtB5Y/EpFVjNETliuv1H+0blK8OPDNN3oi1bMnqwkK4vhxbUldurSOhBUq4H/6QYO0+cSCBdbE55eSAjRt6r2ug25xxx3a9CGvo2qTJwNVqni/gUtMjJbkHTmS8+NefFHfC3fdFZy4Ql2LFvpz79tXF2Hv1k0797rFBx/oHM6uXZ2OxBpM1FyqSBG9WsGGIkRklVWrgO3b3dGWPys1amhTkS1btDV8WprTEXnTgw/qa/3xx0D16gXfX+/e2vTlk08Kvi+/1FS9Ms/5aflXtqyOEk2eDPz8c2DPOXVKRxy6d/d+58OYGP0bsXBh9o/5/XftWnr77ZrUkjXKltV5r2+/rd1HIyN1DqDT/vpLL0YOHqxz1EIBEzUX69RJ/8j88YfTkRBRKPC35e/Wzdk4ctKunc6D+O474IknnI7GeyZNAsaMAYYNs+6KcunSmqx98YWO1llhxQrgn3+YqBXUPfdo9U2go2rz5ukcNS/PT/Nr21ZHi3NaT+3ll/XC9/33By+ucCGicyUXLdLfwSuu0PmwTl5g+/RTPf4NNzgXg9WYqLlYbKzesvyRiKyQnAy0bGnNKIudhg4FbrsNeOkle9fwCjXbtum8jJYtde6IlQYN0hN8f7JfUP65RUzUCqZCBeDuu3Uk+tdfc398UpKWPfvPL7ysbFkdycluntqffwIffaQn7W7/m+dlzZpp87sBA4Cnn9aLOlbPZw2EMVr2GB0NNGwY/OPbhYmaizVsCJx7Lssfiajg9u7VEiE3dXvMyRtv6OjaDTcEXtYVzlJTtUPjqVNakmR1C/KOHfVk16ryx5QU4LzzeAJthfvv19LU55/P+XHGaJnkFVfoKGkoiInRv2snT579tdde0/fFQw8FP65wU6aMjma9+KL+jllZJh2ohQuBdetCp4mIHxM1FxPRq14zZ3KuBhEVzOjReqLmlZKnokWBr74CKlbU9Xvcvm6P00aM0ORn7Fjgggus339EhCaCU6cW/LVIT9dYOZpmjcqVdQ7W558DGzZk/7h163Q6hZe7PWYWE6PluMuWnbl9/36dPzVggHfXivMaEU2K27bVebL79wf3+B98oBcs+vUL7nHtxkTN5WJj9Zd95UqnIyEir5ozR0/kBw3SsjivqFZN11X76y/955ua6nRE7jR3rq5BN2iQfthl0CB9DSZMKNh+1q0D9u1jomYl/0LOOZW8Tp6st14ZVQ/EpZfqbebyxzff1DmQjz4a/JjCWaFCwFtvadfeYP7s//lH/y7166eje6GEiZrLdeyotyx/JKL82LNHOyief76OtnhNq1bAuHHA7NksYcrKvn060lW/vjYRsVPTpjofpaCLX3N+mvWqVdPFnD/9FNi8OevHJCVpa/WaNYMamq3OOQdo0ODMRO3IES2d7tEDuPhi52ILV82aaTfSd9/Vzq7B8NVX+rrfdFNwjhdMTNRcrlo1oEkTNhQhorxLTweuv15H5SdO9O68lMGD9R//qFHOzH1wK2P0xGTXLr2aHIwryYMGAYsXA+vX538fKSl6gn3++dbFRcDDD+uIxksvnf21PXt0HTyvlD7nRfv2wPz5pxdefvdd/Zv32GPOxhXOhg/X5VZuuy04lRAffKAl3+3a2X+sYAsoURORLiKyXkQ2ikiWg5ki0kFEVorIGhFxwWoKoSM2VlvqHjvmdCRE5CWvvaZrJr36qnZH87JXXgEuv1w7Qi5Z4nQ07vDJJ0Biok7gD1ZJ67XXajJQkFE1//w0EeviIh0pu+EGPWndsePMr02dqol9KM1P84uJ0VK7NWuAEyeAkSP1b0WbNk5HFr7KlNF50atW6a2dNmzQ8u8bbwzNvym5JmoiEgFgDICuABoDGCAijTM9pjyAsQB6GGMuAnC19aGGr06d9I9Pdi1oiYgyW7RIryj36gXccYfT0RRckSK6jle1avo97drldETOOnDg9MT9++4L3nGrV9eLh+PHnx7ByIutW/WDZY/2ePRRbT728stnbp88WUc4mjd3Ji47+X+X5s7VCwh//snRNDfo1UvX7HzmGWD7dvuO89FHevFo8GD7juGkQEbUWgPYaIzZZIw5CWACgPhMj7kWwDfGmK0AYIzZbW2Y4a19ez1JYfkjEQXiwAGgf389MXv//dC5yli5si7ovH8/0Ldv1i25w8VTT+n8tDFj9CQlmAYNArZs0UqPvOL8NHvVrauvz7hx2oQH0K6I06dr2WOo/C3IqG5d/Vs3Z46WfbZsqRe4yVkiwP/+p6WPdl1MSkvTRK1rV13OKhQF8ue9BoBtGe5v923LqAGACiIyR0SWiUiI5rXOKFVKF/BjQxEiyo0x2lRg2zZt112hgtMRWSsyUku75s0LzkjS6tXaQe7ECfuPFajly7UxzB13ODNC0rOn/l/Kz3zBlBRdqLhpU8vDIp/HH9eLGK+9pvfnzNGueKFY9ghoQhATo4t+b9yo338oJqReVL++XlT6+mstv7Xa99/rCGqorZ2WUSCJWla/7pnXHC8MoCWAOACdATwlIg3O2pHIUBFZKiJL9+zZk+dgw9mVV2qLfv8VMiKirIwbpx2wRozQsrhQ1L+/Nk546y1tHGC1tDRdFuCKK7SZ091368mGG6SnA3feqaOLzz3nTAylSumI5pdf5n3udEqKTviPiLAnNtKmCv37azK/d692eyxVSudthaqYGL1I1bChXkgg93jwQX1d7roLOHrU2n1/8IH+LQylJScyCyRR2w6gVob7NQH8mcVjvjPG/GOM2QtgLoBmmXdkjBlnjIkyxkRVqVIlvzGHpW7d9HbaNGfjICL3+uUXHWXq3Dn0W9k//7x+n3feqd3srLB/v87tOe88oHdvXRz4xRe1c+bIkdpZzmkffQQsXKjNVcqXdy6OQYOAQ4c0CQjUvn3Ar7+y7DEYnnhCR9Fef11foyuvBIoXdzoq+3TqpMn/U08FvxSYcla0qF40+OMP/bttlb17tZnSoEF6jFAlxmQeHMv0AJHCAH4D0BHADgBLAFxrjFmT4TGNALwJHU0rCmAxgP7GmNXZ7TcqKsosXbq0wN9AuDAGqFVLuxh99ZXT0RCR2/zzDxAVpfPTfv4ZqFrV6Yjs9/ffus7aP/8AS5fqPJX8WLVK51IkJOgIUYcOwD336JyewoWBw4d1baBChfRnW6qUpd9GwPbvBy68UK9Oz53rbHlXWhpQp46WoiYnB/acxEQd7UhJOb1QMdmnb19tInLqlI483HCD0xHZa98+oFIlp6Og7AwerMuI/Pwz0KhRwff3xht6YfKXX7y/Xp6ILDPGRGX1tVyvOxhjUgHcBWA6gLUAvjDGrBGR20TkNt9j1gL4DsAqaJL2Xk5JGuWdiI6qff99eE+gJ6Ks3X23rm01fnx4JGmAzr9LTNREqndvbZoQqNRU4JtvNClr1kyTtOuu06Rt9mztWFa4sD62TBngww+BTZuARx6x5VsJyBNPaHI6Zozzc3AiIvTn9d13wO4A24elpADFimlyTfZ78klN0kSAuDino7EfkzR3GzlSL3LdcYcOPhSEMdooq1Ur7ydpuQlogNgYM9UY08AYc54xZoRv29vGmLczPOYVY0xjY8zFxphRNsUb1uLi9ISEbfqJKKOEBE0knngC6NjR6WiC66KLtKnF4sVaBpnbCcC+fdoZrn59oE8fYPNmLXfcvl3n9zVpkvXzLrtMr96OGeNMB96lS4F33tF5Hm5pxDFokI6sTZgQ2ONTUoDWrTVZI/tFRupctS5dwufiDblX1arACy9oc5uEhILta9kyHUkL5SYifrmWPtqFpY95d+SIXjG6887T3ZyIKLxt2AC0aKHd/2bNOj0KFG6eflqba/zvf5rMZPbzz6fLG48f10Yhd9+t5Y2BNrY4dkx/zkeP6klCuXLWfg/ZSU/Xsvdt24B164J33EC0aKElobn9O//nH51T9/DD2uiGgsN/iuf0CCwRoH/LoqO1OmH9+vx3Jb7jDr04uXOns3N1rVKg0kdyj9KltWvTlClOR0JEbnDiBHDNNTqR+rPPwjdJA4DhwzXpuu8+vWILaHnjV1/paFhkpC5XcP31mmTNnKnzpfLSfbBECR2927EDuP9+y7+FbL33HrBkiZYOuSlJA3TeybJlwNq1OT9u4UJ9PdhIJLhEmKSRexQqpN169+3TZRTy49gx/X/Xt29oJGm5YaLmMXFxwG+/6VohRFRwv/2mI1GrVwN79ugVP6946CFgxQrtBFizptPROKtQIZ2fd8EFwNVXA88+q+WNV1+tI1EjR2p549tvF2xOQ+vWwGOP6dXcvHQ8zK+9e/V4l10GXHut/cfLqwEDNNn99NOcH5eSoq9RdHRw4iIid2reXKsZ3nkHWLQo78//9lvg4MHwKHsEWProOZs2aevoUaOAe+91Ohoib0tL0wQn4/qEERFAlSrAOefoR9Wqpz/PvK1KFaBIEWdinzRJG17cd5+24Ca1fr0mU4cOacvuu+/WC1xWrtt18qROYt+1C1izxt4mBkOHase+n3/W+Xhu1K2bXujYvDn71ugdO2ojlOXLgxoaEbnQoUPa+bFqVa0WyEs1SKdOei68cWPoLMWQU+ljGBfKeFP9+tqaecoUJmpEBbVkiSZpw4frP41du87+WL9eb7PrKFip0pnJW9262gb7wgvti3vrVr2a2LKlrvNFp114ob6u6en6t9IORYvqCFJUlM6VmDjRnuMsWqRlj8OGuTdJA7T8ccAA4Mcfs15U+eRJ4KefgFtuCX5sROQ+ZcvqgEO/ftqgKdDz2c2btWz9P/8JnSQtNxxR86AHH9RJ8Xv3attoIsqfJ5/URGfPnpwnNRujzXyySuR27z7z/pYtOhfnqquABx7QOTlWzhE5dUpbyv/yi5Y9nneedfumvHnhBZ1nMWGCzhW0Ulqajgz+9Zc2EHHz3/pjx/QiRd++OvqX2cKFQNu2wJdf6mOIiIwBunYFFizQOa6BrIM5fLgmaZs3A7Vr2x1h8OQ0osZEzYNmz9aOZd98o6VPRJQ/zZppguZvPmGFXbuAsWP1Y+9eHXV54AE9QbWi2cfjj2uC8Pnn2nqbnJOaqgs3b9igpX/Vq1u377fe0tE6O5JAO9x4oyZiu3YBJUue+bVXXtFuj3/9pQkdEREA/P67Vgv06AF88UXOj01PB+rV00qJ6dODE1+wsOtjiLn0Uh02ZvdH8gpjCr7ApdW2btXFjbt3t3a/55yjjSy2btXGFYcOaVnYeefpshqHDuV/3z/8oCOAN9/MJM0NChcGPv5Y2/UPHWrd7/ju3ZqQX3GFlgZ5weDBOuqcmHj211JStMkLkzQiyui883T9zy+/zD35mjXrdNl/OGGi5kFFigCdOwNTp7rv5Jcos6VLgTp19I+xm/gvdFidqPmVKAHcequWdCQm6ty1Bx4AatXSbo3btuVtf3/9BVx3HdC4MfDGG7aETPlw4YWaPCcna/dNKzz6qCY9b77pndbq7dtrKVLm7o/p6cC8eWzLT0RZe/hhoEEDXSP42LHsH/fBB1oBEx8fvNjcgImaR8XF6UJ/K1Y4HQlR9r7+Wk/gtm3TP7JpaU5HdFpyMnD++fY2/QB0wnOPHtpoYckS7ZD3+uvaGGjgwMC64KWnA4MGAYcPa+OKzKVl5Ky779b2+ffeq3MUC2LBAm39P2yYNrjxikKF9Pd5+vQzu6j++qt2e2SiRkRZKVZMpwr8/ruW9Wfl7791us/AgUDx4sGNz2lM1Dyqa1e90sryR3IjY/QPbt++Og9s9Gidu7JggdORqX/+0c5R3bsHd8QiKkrnlv3+u57cJyVp58bLL9fEMbs13F58EZgxQ3+Obu7+F64KFdLkyhgty8nvWnypqTovrWZN4KmnrI0xGAYN0u/9889Pb0tJ0dv27Z2JiYjcr2NHXSfypZd0bdPMPv8cOHEi/MoeASZqnlW1qq7jw0SN3ObECW1P//jjOjdr9mxgyBC9avb1105Hp2bO1DjtKnvMTZ06Ol9t2zZttLBxo3aJvOgiYNy4M8s/5s8Hnn5a56TddJMz8VLu6tXT13TWLG0Ekh9vvaXrpb3+OlC6tLXxBUOjRnoxImP5Y0oKcO65+vMhIsrOq6/qlIE77jh7Ws/77wORkbpYdrhhouZhcXHA4sU68ZzIDfbuBWJjtcHC8OFAQoKWKZQpo/Mqv/nGHfMqk5O1IY/T5VjlyulyG5s26c/KP6+tTh1tSLJ+vSa7desC77zjnflK4ermm4EuXXTOxYYNeXvurl26XERsLNCnjz3xBcOgQVqSv3q1vtdTUqxfooKIQk+1asDzz+uF1Iyj8itX6hSBcL1QyUTNw7p313+E06Y5HQmRrvXUpo1ePPjsM+CZZ848OevTR0eQlixxLkZAS7OSkzVxLFrU2Vj8ihTRso9ly3REpnVrTXQbNtT5PhMmaGJJ7iaiC1QXLaqjyHmZk/nwwzqS6qUGIlnp3x+IiNBRtS1bgO3bnb8gQkTecOutOio/bBhw4IBu+/BD/Zt67bWOhuYYJmoe1ry5rtvD8kdy2owZmqQdPqyljgMGnP2Yq67SduZOlz+uWKGNeJwqe8yJyOn5ar/+Ctx1l3YSjMpydRVyoxo1gDFjdD7mq68G9pyUFOCTT7QbaIMG9sZnt6pVdQ51QoI20AGYqBFRYCIidFmbPXu0wuDECWD8eF0zuGJFp6NzBhM1DxPRDnLTpwOnTjkdDYWrceO03KtWLWDRIqBt26wfV6GCThj++mtnyx+Tk0+/d9ysUSPgf/8L36uIXjZggI4gP/WUlgDm5NQpnZNRu7bO6wwFgwYBO3ZoQ6Hy5YGLL3Y6IiLyipYttVX/2LH6N3T//vBsIuLHRM3j4uJ0Ad35852OhMJNWpqWJ9x6K3Dllfo7WLduzs/p00c7Hq5aFZQQs5SUpMlk5crOxUChTUQbg5QrpwtB53QhbcwYTeZGjQJKlQpaiLa66iot1V2/HmjXTrtiEhEF6rnngHPO0WZbtWrpRd5wxT+fHtepk85vYfkjBdPhw0DPntqd7p57gMmTA5tD1bOnnrQ5Vf745586D8yNZY8UWqpU0dHmFSuAESOyfsyff2pHz65d9b0RKkqUAPr1089Z9khEeVWunJ5fADrfNyLC0XAcxUTN48qU0YVWk5OdjoTCxdatwKWXahObMWOAN97QuWeBqFJF11NyKlGbOlVvmahRMPTsqWWA//2vXiDI7KGHdA7G6NHebiCSlaFDteOr20uMicidrrlGLwI//LDTkTiLiVoI6N5dO+5t2uR0JBTqFi/WjoSbN+so7h135H0fffpoo4x16ywPL1dJSdr6nnNmKFjeeEPbTg8eDBw/fnr7nDnaHfWRR4Dzz3csPNu0aqULyzdp4nQkRORFIlpG7cU1Ja3ERC0ExMXpLcsfyU5ffqmjtyVKaEe7zp3zt59evfQ22KNqx45pd8ru3UNv9ILcq0IFXaz111+1zBHQOWt33qlzOh97zNHwbMW5aUREBcM/oyHg/PO1pTMTNbKDMTrHpl8/oEULHVW76KL8769GDW3lH+xEbc4c4OhRlj1S8HXurE13Ro7UpjtvvKGJ2+jReuGDiIgoK0zUQkRcnJ6I/vOP05FQKDlxQku2nnwSuO46YOZMnWdWUH36aJOFYJbrJiVpV70OHYJ3TCK/V17REbRBg3Qx8+7dtayHiIgoO0zUQkRcnJ5Uz5zpdCQUKvbs0Za448drq9xPPtHmAFbo00dvv/nGmv3lxhhtuBMba933QJQXZcro4uWbN+vSFqNHOx0RERG5HRO1EBEToycC7P5IVti6FbjkEu1UN3GijqhZOa+rXj2gefPglT/+8guwbRvLHslZ7dvrfLXx4/U9QERElJMAm2qT2xUtqosOT52qowdslkAF8b//Adu3AykpmrDZoU8fTQC3bwdq1rTnGH5JSXrrb7xD5JQbbnA6AiIi8gqOqIWQuDhgxw7g55+djoS8zBggMVHnctmVpAGnyx+//da+Y/glJ2u78GrV7D8WERERkRWYqIWQrl31lt0fqSDWrQM2bADi4+09TsOGQOPG9s9T270bWLSIZY9ERETkLUzUQki1akBUFBM1KpjERL3t0cP+Y/XpA8ydq41L7OIvB2aHPSIiIvISJmohJi4OWLgQ2LvX6UjIqxITgZYtgVq17D9Wnz5AejowaZJ9x0hOBs49F4iMtO8YRERERFZjM5EQ07078OyzwLRpul6P16SlAa1bawvrKlXO/Kha9extVaoAlStrMxUquL/+0jLBZ58NzvGaNgXOO0+7P95yi/X7P3kSmD4duPZaNtghIiIib2GiFmJatADOOUfLH72YqM2ZAyxfDvTsqcnXnj3Ab78B8+frKGF6etbPK18+6ySualXgwguBLl14oh6IpCQtE7R7fpqfiI6qvfYa8PffQIUK1u7/xx+BI0dY9khERETew0QtxBQqBHTrpp30UlOBwh57hcePB8qWBT7//OyFidPT9WR+925N4Pwfme///vvp8s+0NH1ujx7Ae+9p8kbZS0wE6tYFmjQJ3jH79AFeflmTxMGDrd13crL+Hl1xhbX7JSIiIrKbx07jKRBxccCHHwILFugCq15x7JiWwF199dlJGqBJaKVK+tGoUe77S08HDhwAPv4YeOwxTT4++EATWTrbkSPAjBnAbbcFd/SxVSudD/f119YmasZo8texI1CypHX7JSIiIgoGNhMJQbGxQJEi3uv+mJQEHD4MXHedNfsrVAioWBG4/35gyRItg4yLA+68Ezh61JpjhJLvvwdOnAhe2aOfCNC7t84lO3zYuv2uXQv88QfLHomIiMibmKiFoLJlgZgY7yVq48cDNWoAl11m/b6bNAEWLwaGDQPGjtW5fMuWWX8cL0tM1DliMTHBP3afPpokTp1q3T6Tk/U2Ls66fRIREREFCxO1ENW9O7BmjXZP9IK9e7VT5bXX6kiYHYoXB159Vcv7jhwB2rQBXnjh9Dy2cJaaqolNXJwz8xqjo3XE8+uvrdtncrK25K9Z07p9EhEREQULE7UQ5R9F8Mqo2pdfarJgVdljTjp2BFatAnr1Ah5/HLj8cu8ktHaZNw/Yvz/4ZY9+ERH6ekydqnMVC2rfPu0UyrJHIiIi8iomaiGqQQPg/PO9k6iNHw9cfLGuqxUMFSsCEycCn3wCrFwJNGumMRgTnOO7TWKiLofQubNzMfTpA/zzj85VK6jvvtNmMt27F3xfRERERE5gohbC4uKA2bPd3zhj0ybtUBmM0bSMRHStuVWrNEEcNAgYMECXAAgnxmii1rEjUKaMc3F06KBz5Kwof0xO1lLKqKiC74uIiIjICQElaiLSRUTWi8hGEXk0i693EJGDIrLS9/G09aFSXsXFAcePA7NmOR1Jzj77TG+vvdaZ49etqwttP/+8JglNm7r/Z2al1au1O2LPns7GUaSIll4mJQEnT+Z/P6dO6XzHuDj75jsSERER2S3X0xgRiQAwBkBXAI0BDBCRxlk8NMUYE+n7+I/FcVI+tG8PlCrl7vJHY7Tk8LLLdC0tp0RE6FprP/2ka2517Ag8+KB2Igx1iYl664b5XH36AAcPAjNn5n8f8+frPtzw/RARERHlVyDXm1sD2GiM2WSMOQlgAgCHWg5QXhQrBlx5pSZqbp17tXw5sH598MsesxMVpTHdfrt2iGzdWkecQlliInDJJUD16k5HomsAlilTsPLH5GSdb9epk3VxEREREQVbIIlaDQDbMtzf7tuWWVsR+VlEponIRZZERwUWFwds2wb88ovTkWRt/Hg9qe7b1+lITitVStdaS04G/vpLk7dRo7Q5RajZsQNYutS5bo+ZFSumDUAmTdIuoPmRlKTz3Zycb0dERERUUIEkapLFtszjM8sB1DHGNAPwPwCTstyRyFARWSoiS/fs2ZOnQCl/unXTWzeWP6amAp9/rifm5cs7Hc3Z4uI0wb3ySuD++7Uj4o4dTkdlrcmT9dYtiRqg5Y/79gFz5+b9ub/9ph8seyQiIiKvCyRR2w4g4+yhmgD+zPgAY8whY8wR3+dTARQRkcqZd2SMGWeMiTLGRFWpUqUAYVOgqlcHWrRwZ6I2cyawa5d7yh6zUrWqlga+8452pmzSRNccCxWJibqMQ6NGTkdyWpcuQIkSwDff5P25/t9z/zqCRERERF4VSKK2BMAFIlJPRIoC6A9gcsYHiEg1ERHf5619+91ndbCUP3Fx2iRjn8tekYQEHUnzj/q5lQgwdCiwYoWWaY4a5XRE1jh0SLtbxsfr9+gWpUoBXbtqopbXctOkJOCii4B69eyJjYiIiChYck3UjDGpAO4CMB3AWgBfGGPWiMhtInKb72F9AawWkZ8BjAbQ3xi3tq8IP3FxesJrxULCVvnnHz0Rv/pqnZfkBQ0aaIOKBQvc25wlL777TlvZu6ns0a9PH2DnTmDhwsCfc+AAkJLCskciIiIKDQGtMmSMmWqMaWCMOc8YM8K37W1jzNu+z980xlxkjGlmjGljjFlgZ9CUN61aAVWquKv8MTFRkzU3lz1mJTpaE4itW52OpOASE4HKlfV7cpvu3XX0Mi/dH7//Xuc9du9uX1xEREREwcLlYMNAoUJaXjhtWv476VktIQGoXRu49FKnI8mbtm31doHHL0WcOgVMnapJTUSE09GcrWxZbdX/9deBj14mJQGVKgFt2tgbGxEREVEwMFELE3FxwN9/562UzC67d2sZ5sCBmkR6SZMmOofK64na3LlaKujGske/Pn2ALVt0XbvcpKVp4tmtmzsTTyIiIqK88thpMuXXlVcChQu7o/xx4kQ9sR440OlI8q5wYV0E+6efnI6kYBITgeLFddTKrXr00KQrkPLHhQuB/ftZ9khEREShg4lamChXTssM3ZCoJSQAkZHanc+LoqOBlSt1jp0XGaOJWmysjg66VaVKwOWXB1b+mJSkSXTnzsGJjYiIiMhuTNTCiH8BZycbYWzYACxa5L0mIhm1basjgkuXOh1J/qxcqb8DPXs6HUnuevfWBazXrMn5ccnJQPv2ekGCiIiIKBQwUQsj/rKwqVOdiyEhQdfs6t/fuRgKyt+swqvz1BIT9TXwQplgr14aa07lj3/8oYmcF74fIiIiokAxUQsjF14I1K+vow9OMEYTtSuuAGrUcCYGK1SqpD9Lr85TS0zU8s2qVZ2OJHfVqgHt2uWcqPl/n5moERERUShhohZGRLT8cdYs4Nix4B9/8WJg40Zvlz36RUdroua1ha+3bNHSRzd3e8ysTx8t2d2wIeuvJydr4nzBBcGNi4iIiMhOTNTCTFycJmmzZwf/2OPHa6fB3r2Df2yrtW0L7N2riaeXTJ6st15K1Py/L1mNqh0+DMyZw9E0IiIiCj1M1MLMZZcBJUsGv/vjqVPalr9HD13M2Ouio/XWa/PUEhOBhg2BBg2cjiRwtWsDrVplnajNmAGcPMlEjYiIiEIPE7Uw4187a8qU4Jbt/fADsGdPaJQ9AkCjRtph0Evz1A4cAH780VujaX59+miXzS1bztyelASUL6/z2IiIiIhCCRO1MBQXpye8v/4avGOOHw9UrBg661wVKqTdH700ojZ1KpCa6t1EDQC++eb0tvR0veDQpQtQpIgzcRERERHZhYlaGOrWTW8/+ig4xzt8GJg0CbjmGqBo0eAcMxiio4HVq4FDh5yOJDCJicA55wCXXOJ0JHl3/vlA06Znlj8uXQrs3s2yRyIiIgpNTNTCUI0awPXXAyNH5tz23CqTJmkDk1Ape/Rr21bLRxctcjqS3J04AUybBlx1lY4GelGfPjqCuXOn3k9K0u+la1dn4yIiIiKyg0dP2aig3n5bS/cGDwZWrLD3WOPHA/XqaWITSi65RJc88MI8tTlzdGTTi2WPfn36aGI8aZLeT07WuWkVKzoaFhEREZEtmKiFqeLFgW+/1cWbe/Q4PUphtb/+0s58AwdqUhNKypYFLr7YG/PUEhO122fHjk5Hkn+NG+t6aV9/DWzfruvBseyRiIiIQhUTtTBWrZquq7V/P9CrF3D8uPXHmDBBmz4MHGj9vt0gOhpYuFC/R7cyRl/nLl2AEiWcjib/RHRUbc4c4OOPdRsTNSIiIgpVTNR8EhKAunV1zkvduno/HERGamniokXATTdZ37J//HigZUtduysUtW0LHDwIrF3rdCTZW7YM2LHD22WPfn36AGlpwIgRQP36ukwCERERUShiogZNyoYO1Zb1xujt0KHhk6z16qUnvp99Bjz/vHX7XbdOk4RQayKSkRcWvk5MBCIidFkGr2veXC+kHDumo2mhVk5LRERE5MdEDcATTwBHj5657ehR3R4uHntMyxOffPLMtaoKIiFBRyj797dmf250/vlA5crubiiSmAhceqnOR/Q6f/kjwLJHIiIiCm2FnQ7ADbZuzdv2UCQCvPcesHEjMGiQdmls3jz/+zNGyx47ddK5cKFKRMsf3TqitmkT8MsvwGuvOR2Jde67T5vhXH6505EQERER2YcjagBq187b9lBVvLi2Pvd3gvzrr/zva8ECYPPm0C579GvbFli/Hti3z+lIzpaYqLehMD/Nr2ZN4L//BQrzMhMRERGFMCZq0PlZJUueua1kSd0ebjJ2guzZM/+dIBMS9GfYq5el4bmSf57awoXOxpGVxERdQqB+facjISIiIqK8YKIGnZs1bhxQp46WstWpo/dDtaV8biIjgU8/zX8nyJMngYkTdRSndGlbQnSVqCht1uG2eWr79gEpKaE1mkZEREQULpio+QwcqKV66el6G65Jml/v3lpe9tlnwAsv5O25332nI3LhUPYIAKVKaXLrtnlqU6bo7zMTNSIiIiLvYaJG2Xr8ceDaa7X75bffBv68hASgShUgNta+2NymbVtg8WIgNdXpSE5LTATOPVfXsSMiIiIib2GiRtnyd4K85BIdHVuxIvfnHDyoc9z69weKFLE/RreIjgb++Uc7LLrB8ePA9OnaFKYQ3+VEREREnsNTOMpRiRLaCbJixcA6QX7zjSYJ4VY66m8o4pZ5ajNnauLIskciIiIib2KiRrnKSyfI8eN1EejWrYMWnivUrg1Ur+6eeWqJiUCZMlxrjIiIiMirmKhRQJo3P90J8uabs+4EuWMHMHu2lkmKBD9GJ4noqJobRtTS04GkJKBrV6BYMaejISIiIqL8YKJGAfN3gkxIAF588eyvf/65JnDhVvbo17YtsGkTsGuXs3EsXqwlqix7JCIiIvIuJmqUJ/5OkI8/fnYnyPHjtfHI+ec7E5vT3DJPLTERKFwY6NbN2TiIiIiIKP+YqFGe+DtBtm6tJY4rV+r21auBn38On7XTstKiBVC0qPPz1BITgcsuA8qXdzYOIiIiIso/JmqUZ1l1gkxIACIigH79nI7OOcWK6ZplTo6obdgArF3LskciIiIir2OiRvlSvbp2gty3D+jVSxO1zp2BqlWdjsxZbdsCS5YAJ086c/zERL3t0cOZ4xMRERGRNZioUb75O0EuXAhs2xbeZY9+0dHAiROBLQ5uh8REIDISqFPHmeMTERERkTWYqFGB9O4NjBwJNGvGURxAR9QAZ8ofd+/W+XEseyQiIiLyPiZqVGAPPKBNRUqVcjoS5517ro5mOdFQJDlZ11BjokZERETkfUzUiCzWtq0zI2qJiUDt2lr6SERERETexkSNyGLR0cD27TpvL1h27wamTwd69tQlFIiIiIjI2wJK1ESki4isF5GNIvJoDo9rJSJpItLXuhCJvMWJeWrvvKNNTG6/PXjHJCIiIiL75JqoiUgEgDEAugJoDGCAiDTO5nEvAZhudZBEXtKsma41F6x5aidOAGPHAl27Ag0bBueYRERERGSvQEbUWgPYaIzZZIw5CWACgKzaFdwN4GsAuy2Mj8hzihQBWrUK3ojaF1/oouP33Rec4xERERGR/QJJ1GoAyDjbZrtv279EpAaAXgDezmlHIjJURJaKyNI9e/bkNVYiz4iOBpYvB44ds/c4xgCvvw40agTExtp7LCIiIiIKnkAStaxaE5hM90cBeMQYk5bTjowx44wxUcaYqCpVqgQYIpH3REcDqanAsmX2HmfePF1c+7772ESEiIiIKJQUDuAx2wHUynC/JoA/Mz0mCsAE0TPFygC6iUiqMWaSFUESeU2bNnq7YAFw6aX2HWfUKKBiReC66+w7BhEREREFXyCJ2hIAF4hIPQA7APQHcG3GBxhj6vk/F5GPACQzSaNwVqUKcMEF9s5T++MPYNIk4JFHgJIl7TsOEREREQVfrqWPxphUAHdBuzmuBfCFMWaNiNwmIrfZHSCRV7VtqyNqJnOhsEXefBMoVAi44w579k9EREREzglkRA3GmKkApmbalmXjEGPMkIKHReR90dHAJ5/oyFf9+tbu+/Bh4L33gKuvBmrWtHbfREREROS8gBa8JqK88y98bcd6ah99BBw6xJb8RERERKGKiRqRTS66CChTxvpELT0deOMNTQRbt7Z230RERETkDkzUiGwSEQFccon1DUWmTAF+/52jaUREREShjIkakY2io4FVq3ROmVVGjQJq1QJ697Zun0RERETkLkzUiGzUtq2WKi5ZYs3+Vq0CZs0C7roLKBxQKyAiIiIi8iImakQ2yrjwtRXeeEPXTLv5Zmv2R0RERETuxESNyEblywONG1szT233biAhAbj+eqBixYLvj4iIiIjci4kakc2iozVRS08v2H7efhs4cQK45x5r4iIiIiIi92KiRmSztm2Bv/8Gfvst//s4cQIYOxbo2hVo2NC62IiIiIjInZioEdksOlpvCzJPbeJEYNcutuQnIiIiChdM1Ihs1qABUKFC/uepGaMt+Rs1AmJjLQ2NiIiIiFyKDb6JbFaokJY/5ndELSUFWLECeOcdQMTa2IiIiIjInTiiRhQEbdsCv/4KHDiQ9+eOGqVdHq+7zuqoiIiIiMitmKgRBYF/ntrChXl73qZNwKRJwK236vppRERERBQemKgRBUHr1loCmdd5am++CUREAHfcYU9cREREROROTNSIgqB0aaBp07zNUzt0CHjvPeDqq4GaNe2LjYiIiIjch4maTRISgLp1dRSlbl29T+EtOhpYtAhISwvs8R99BBw+zJb8REREROGIiZoNEhKAoUOBLVu0tfqWLXqfyVp4a9tWE681a3J/bFoaMHq0Pqd1a/tjIyIiIiJ3YaJmgyeeAI4ePXPb0aO6ncJXXha+njIF+P13jqYRERERhSsmajbYujVv2yk81KsHVK0aWEORUaOAWrWA3r1tD4uIiIiIXIiJmg1q187bdgoPIjqqltuI2s8/A7NnA3ffDRTmkvREREREYYmJmg1GjDh7zauSJXU7hbe2bYGNG4E9e7J/zBtv6O/LzTcHLy4iIiIichcmajYYOBAYNw6oU0dHUerU0fsDBzodGTnNP08tu/LH3bu16cyQIUCFCkELi4iIiIhchomaTQYOBDZvBtLT9ZZJGgFAy5Zazphdovb228DJk8A99wQ3LiIiIiJyFyZqREFUogTQokXW89ROnADGjgW6dQMuvDD4sRERERGRezBRIwqytm2BJUuAU6fO3D5xIrBrF1vyExERERETNaKgi44Gjh3T7o5+xmhL/saNgU6dHAuNiIiIiFyCiZoHJCQAdesChQrpbUKC0xFRQbRtq7cZ56mlpAArVuhomogjYRERERGRizBRc7mEBGDoUGDLFh112bJF7zNZ865atYCaNc+cpzZqFFCpEnDddY6FRUREREQuwkTN5Z54Ajh69MxtR4/qdvKutm1Pj6ht2gRMmgTceqs2GyEiIiIiYqLmclu35m07eUN0tI6O/vkn8OabQEQEcMcdTkdFRERERG7BRM3latfO23byBv/C19OnA++9B/TrB9So4WxMREREROQeTNRcbsQIoGTJM7eVLKnbybsiI4HixYFHHwUOHwbuvdfpiIiIiIjITZioudzAgcC4cUCdOtoNsE4dvT9woNORUUEULQpERQG7d+voWuvWTkdERERERG5S2OkAKHcDBzIxC0Vt2wLz5nGBayIiIiI6GxM1IofcfLPe9urlbBxERERE5D5M1Igc0qAB8PLLTkdBRERERG7EOWphKCEBqFsXKFRIb7l4NhERERGRu3BELcwkJABDh55eRHvLFr0PcB4cEREREZFbcEQtzDzxxOkkze/oUd1ORERERETuEFCiJiJdRGS9iGwUkUez+Hq8iKwSkZUislRELrU+VLLC1q15205ERERERMGXa6ImIhEAxgDoCqAxgAEi0jjTw2YCaGaMiQRwI4D3LI6TLFK7dt6258bq+W6cP0dEREREFNiIWmsAG40xm4wxJwFMABCf8QHGmCPGGOO7WwqAAbnSiBFAyZJnbitZUrfnlX++25YtgDGn57vlN7myen9ERERERF4VSKJWA8C2DPe3+7adQUR6icg6AFOgo2pnEZGhvtLIpXv27MlPvFRAAwcC48YBdeoAIno7blz+GolYPd+N8+eIiIiIiJScHgjL5gEiVwPobIy52Xd/EIDWxpi7s3l8ewBPG2M65bTfqKgos3Tp0vxFTa5QqJCOfGUmAqSnO78/IiIiIiI3E5FlxpiorL4WyIjadgC1MtyvCeDP7B5sjJkL4DwRqZynKMlzrJ7vZvX+iIiIiIi8KpBEbQmAC0SknogUBdAfwOSMDxCR80VEfJ+3AFAUwD6rgyV3sXK+mx37IyIiIiLyqlwTNWNMKoC7AEwHsBbAF8aYNSJym4jc5ntYHwCrRWQltEPkNSa3mkryPCvnu9mxPyIiIiIir8p1jppdOEeN7JaQoI1Itm7V8skRI5j0EREREZF75DRHrXCwgyEKBn+rf38XSX+rf4DJGhERERG5XyBz1Ig8x45W/1yMm4iIiIiChSNqFJK2bs3b9txwhI6IiIiIgokjahSSrG71z8W4iYiIiCiYmKhRSLK61b/VI3RWY1kmERERUWhhokYhyepW/25ejNtflrllC2DM6bJMJmtERERE3sVEjULWwIHA5s1AerreFmQumdUjdFaOgLEsk4iIiCj0MFEjCoCVI3RWj4C5vSyTiIiIiPKOC14TBVndupqcZVanjo78Ob0/IiIiIgqOnBa85ogaUZBZPQJmdVkmERERETmPiRpRkFndmMTqxilWY0dKIiIiorxjokYUZHaMgFnZOMXKxMrtHSmZRBIREZFbMVEjCjI3j4BZnVhZ3ZEynJJIIiIiCm9sJkJE/7K6MUmhQpoEZSaio3954U+sMiZ+JUvmP8llExYiIiJyGpuJEFFArG50YuV8PKtH57isAREREbkZEzUi+pfVjU6snI/n5iQScPd8NzfHRkRERFljokZE/7K60YmV8/HcnES6eb6bm2MD3J1Eujk2IiIKfZyjRkRnSEjQcsKtWzUJGjHCXY1OrJqj5t+nFd+rm+e7uTk2O15Tq7g5NiIiCh05zVFjokZEnuHWJNLKpil+Vn2vdsRmFTcnkW6OjYiIQgcTNSIiG1l9Um/laI6bEw43J5Fujo2IiEIHuz4SEdnI6rl9Vna4tGOBdatYPe/QSm6OjYiIwgMTNSKiArJ6EXMrO1y6eYF1O5JIqxqAuDnBJSKi8MBEjYjIAgMHailherreFiQRsno0x8rYrGR1Emllh0s7Elwru0iyI6U78HUgIjsxUSMichm3j+ZYeXJqZRJp9aLoVsZmZRLJJRcKxqr43P46EJH3sZkIEZELubXDpZvb1ru5AYiVTV3c3CDGzb8fQPg06iEi72DXRyIisoSbT07dHJuVSWS4JKR2sDI+N78OROQd7PpIRESWsLLRidXcXDJq5bxDN3ekdPPvB2BtfG5+Hezg9pJWolDERI2IiALm5pPTcOlw6eZumXb8fliZIFgZn5tfB6txPh6RQ4wxjny0bNnSEBGRt4wfb0zJksbo6Zp+lCyp2yln48cbU6eOMSJ6W5CfmdX7suo1tfr3wwv7c+PrYLU6dc6My/9Rp47TkSkrXweiYAOw1GSTLzFRIyKiPOFJUWix+iTcyt8POxIEt/7+uvl1EMk6NhHnY3NzgksUiJwSNTYTISIiCmNuborh5tisZuX3anX3TSubsLg5NiInsJkIERERZcnN8w7dHJvVrPxerV5T0Mr5eFbHZnUDG7fOE6TwxESNiIgojLm5W6abY7Oald+r1cmLlY16rI7NygTXjqYpTPyoIJioERERhTE3d8t0c2xWs/J7tWMkcuBALSVMT9fb/L4GVsfm5tE+dsukguIcNSIiIqIQYvU8MCvZEVtCgiZTW7dqwjdiRP72ZfWcSLfPn7Pq50YFwzlqRERERGHCzSORdsTm1tE+Ny8Ab/Von9Ulnlbuz8vlpxxRIyIiIqKwF04dKd3cydPK/bl5dNkvpxE1JmpERERERLC2HNDNSYKVZZ5WJ6RW7s/NybIfSx+JiIiIiHJhVRmlf19WlnlaWcJnZZmn1SWeVu7PzeWngWCiRkRERERkA6sSP6vnlFnZLdPquX1W7s/razEGlKiJSBcRWS8iG0Xk0Sy+PlBEVvk+FohIM+tDJSIiIiIKP1YvHWDlaJ/V6x1auT+vr8WYa6ImIhEAxgDoCqAxgAEi0jjTw/4AcJkxpimA5wCMszpQIiIiIqJwZEcJn1WjfVaXeFq5Pzd3QA1Ers1ERKQtgOHGmM6++48BgDHmhWweXwHAamNMjZz2y2YiRERERES580JTDMqfgjYTqQFgW4b7233bsnMTgGnZBDJURJaKyNI9e/YEcGgiIiIiovDm9RI+yp9AEjXJYluWw3Aicjk0UXskq68bY8YZY6KMMVFVqlQJPEoiIiIiojDl9RI+yp/CATxmO4BaGe7XBPBn5geJSFMA7wHoaozZZ014REREREQ0cCATs3ATyIjaEgAXiEg9ESkKoD+AyRkfICK1AXwDYJAx5jfrwyQiIiIiIgofuY6oGWNSReQuANMBRAD4wBizRkRu8339bQBPA6gEYKyIAEBqdpPiiIiIiIiIKGe5dn20C7s+EhERERFROCto10ciIiIiIiIKIiZqRERERERELsNEjYiIiIiIyGWYqBEREREREbkMEzUiIiIiIiKXYaJGRERERETkMkzUiIiIiIiIXMaxddREZA+ALRbtrjKAvRbti/KHr4E78HVwHl8Dd+Dr4Dy+Bu7A18Ed+Do4z62vQR1jTJWsvuBYomYlEVma3UJxFBx8DdyBr4Pz+Bq4A18H5/E1cAe+Du7A18F5XnwNWPpIRERERETkMkzUiIiIiIiIXCZUErVxTgdAfA1cgq+D8/gauANfB+fxNXAHvg7uwNfBeZ57DUJijhoREREREVEoCZURNSIiIiIiopDh6URNRLqIyHoR2SgijzodT7gSkc0i8ouIrBSRpU7HEy5E5AMR2S0iqzNsqygiP4jIBt9tBSdjDHXZvAbDRWSH7/2wUkS6ORljqBORWiIyW0TWisgaEbnXt53vhSDK4XXg+yFIRKS4iCwWkZ99r8Gzvu18LwRRDq8D3wtBJiIRIrJCRJJ99z33XvBs6aOIRAD4DUAsgO0AlgAYYIz51dHAwpCIbAYQZYxx49oUIUtE2gM4AuATY8zFvm0vA9hvjHnRd/GigjHmESfjDGXZvAbDARwxxox0MrZwISLVAVQ3xiwXkTIAlgHoCWAI+F4Imhxeh37g+yEoREQAlDLGHBGRIgDmAbgXQG/wvRA0ObwOXcD3QlCJyDAAUQDKGmO6e/Ecycsjaq0BbDTGbDLGnAQwAUC8wzERBY0xZi6A/Zk2xwP42Pf5x9ATJbJJNq8BBZExZqcxZrnv88MA1gKoAb4XgiqH14GCxKgjvrtFfB8GfC8EVQ6vAwWRiNQEEAfgvQybPfde8HKiVgPAtgz3t4P/FJxiAHwvIstEZKjTwYS5c4wxOwE9cQJQ1eF4wtVdIrLKVxrp+tKKUCEidQE0B7AIfC84JtPrAPD9EDS+Uq+VAHYD+MEYw/eCA7J5HQC+F4JpFICHAaRn2Oa594KXEzXJYhuvWDijnTGmBYCuAO70lYMRhau3AJwHIBLATgCvOhpNmBCR0gC+BnCfMeaQ0/GEqyxeB74fgsgYk2aMiQRQE0BrEbnY4ZDCUjavA98LQSIi3QHsNsYsczqWgvJyorYdQK0M92sC+NOhWMKaMeZP3+1uAN9Cy1LJGbt8c0X8c0Z2OxxP2DHG7PL9k04H8C74frCdbx7I1wASjDHf+DbzvRBkWb0OfD84wxhzAMAc6LwovhcckvF14HshqNoB6OHroTABwBUiMh4efC94OVFbAuACEaknIkUB9Acw2eGYwo6IlPJNHIeIlAJwJYDVOT+LbDQZwPW+z68HkOhgLGHJ/0/Apxf4frCVb+L++wDWGmNey/AlvheCKLvXge+H4BGRKiJS3vd5CQCdAKwD3wtBld3rwPdC8BhjHjPG1DTG1IXmB7OMMdfBg++Fwk4HkF/GmFQRuQvAdAARAD4wxqxxOKxwdA6Ab/V/NAoD+MwY852zIYUHEfkcQAcAlUVkO4BnALwI4AsRuQnAVgBXOxdh6MvmNeggIpHQUuzNAG51Kr4w0Q7AIAC/+OaEAMDj4Hsh2LJ7HQbw/RA01QF87OuKXQjAF8aYZBH5CXwvBFN2r8OnfC84znP/Fzzbnp+IiIiIiChUebn0kYiIiIiIKCQxUSMiIiIiInIZJmpEREREREQuw0SNiIiIiIjIZZioERERERERuQwTNSIiIiIiIpdhokZEREREROQyTNSIiIiIiIhc5v9PNq3qwwOyCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "66cf1ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator(data, lookback, delay, min_index, max_index, \n",
    "              shuffle=False, batch_size=128, step=6):\n",
    "    if max_index is None:\n",
    "        max_index = len(data) - delay - 1\n",
    "    i = min_index + lookback\n",
    "    while 1:\n",
    "        if shuffle:\n",
    "            rows = np.random.randint(\n",
    "                min_index + lookback, max_index, size=batch_size)\n",
    "        else:\n",
    "            if i + batch_size >= max_index:\n",
    "                i = min_index + lookback\n",
    "            rows = np.arange(i, min(i + batch_size, max_index))\n",
    "            i += len(rows)\n",
    "        samples = np.zeros((len(rows),\n",
    "                            lookback // step,\n",
    "                            data.shape[-1]))\n",
    "        targets = np.zeros((len(rows),))\n",
    "        for j, row in enumerate(rows):\n",
    "            indices = range(rows[j] - lookback, rows[j], step)\n",
    "            samples[j] = data[indices]\n",
    "            targets[j] = data[rows[j] + delay][0]\n",
    "        yield samples[:, ::-1, :], targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6babf819",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "500/500 [==============================] - 8s 9ms/step - loss: 0.3896 - val_loss: 0.6698\n",
      "Epoch 2/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2694 - val_loss: 0.4583\n",
      "Epoch 3/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2632 - val_loss: 0.4231\n",
      "Epoch 4/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2558 - val_loss: 0.4928\n",
      "Epoch 5/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2574 - val_loss: 0.4950\n",
      "Epoch 6/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2494 - val_loss: 0.3536\n",
      "Epoch 7/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2399 - val_loss: 0.4024\n",
      "Epoch 8/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2298 - val_loss: 0.5416\n",
      "Epoch 9/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2316 - val_loss: 0.4131\n",
      "Epoch 10/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2293 - val_loss: 0.5465\n",
      "Epoch 11/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2236 - val_loss: 0.3876\n",
      "Epoch 12/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2173 - val_loss: 0.5662\n",
      "Epoch 13/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2079 - val_loss: 0.5028\n",
      "Epoch 14/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2021 - val_loss: 0.4924\n",
      "Epoch 15/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.2042 - val_loss: 0.5413\n",
      "Epoch 16/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.1904 - val_loss: 0.5312\n",
      "Epoch 17/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.1937 - val_loss: 0.5272\n",
      "Epoch 18/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.1738 - val_loss: 0.5621\n",
      "Epoch 19/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.1842 - val_loss: 0.4988\n",
      "Epoch 20/20\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.1708 - val_loss: 0.7015\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(layers.GRU(32, input_shape=(None, float_data.shape[-1])))\n",
    "model.add(layers.Dense(1))\n",
    "\n",
    "model.compile(optimizer=RMSprop(), loss='mae')\n",
    "history = model.fit(train_gen,\n",
    "                    steps_per_epoch=500,\n",
    "                    epochs=20,\n",
    "                    validation_data=val_gen,\n",
    "                    validation_steps=val_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "124c9765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2ccfb6de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "500/500 [==============================] - 8s 8ms/step - loss: 0.4757 - val_loss: 0.9933\n",
      "Epoch 2/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.2978 - val_loss: 0.4605\n",
      "Epoch 3/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.2653 - val_loss: 0.4445\n",
      "Epoch 4/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.2594 - val_loss: 0.4964\n",
      "Epoch 5/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.2478 - val_loss: 0.5174\n",
      "Epoch 6/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.2471 - val_loss: 0.5129\n",
      "Epoch 7/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.2353 - val_loss: 0.5023\n",
      "Epoch 8/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.2266 - val_loss: 0.5197\n",
      "Epoch 9/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.2096 - val_loss: 0.5353\n",
      "Epoch 10/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.2089 - val_loss: 0.5532\n",
      "Epoch 11/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.2014 - val_loss: 0.5776\n",
      "Epoch 12/20\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 0.1950 - val_loss: 0.5957\n",
      "Epoch 13/20\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 0.1785 - val_loss: 0.7554\n",
      "Epoch 14/20\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 0.1852 - val_loss: 0.6714\n",
      "Epoch 15/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.1904 - val_loss: 0.5756\n",
      "Epoch 16/20\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 0.1802 - val_loss: 0.6010\n",
      "Epoch 17/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.1668 - val_loss: 0.5705\n",
      "Epoch 18/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.1697 - val_loss: 0.5958\n",
      "Epoch 19/20\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 0.1680 - val_loss: 0.5236\n",
      "Epoch 20/20\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 0.1554 - val_loss: 0.5486\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(layers.LSTM(32))\n",
    "model.add(layers.Dense(1))\n",
    "\n",
    "model.compile(optimizer=RMSprop(), loss='mae')\n",
    "history = model.fit(train_gen,\n",
    "                    steps_per_epoch=500,\n",
    "                    epochs=20,\n",
    "                    validation_data=val_gen,\n",
    "                    validation_steps=val_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "abd41c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac82f806",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
